The most frequent killer of Americans is coronary artery disease (CAD) and heart-related problems, accounting for nearly 600,000 deaths each year. Studies estimate that 50% of men and 33% of women under the age of 45 will develop some form of CAD sometime during their lifetime. Sudden cardiac death has steadily accounted for approximately 50% of all heart-related, out-of-hospital deaths and improved clinical procedures almost entirely ignore this group. The fact that patients generally fail to recognize their symptoms and to seek prompt medical attention contributes to these tragic statistics.
The cardiovascular system has three components: (1) a pump (or heart); (2) a carrier fluid (or blood); and (3) a distribution system (or arteries), an exchange system (or capillary network) and a collecting system (or venous system). Blood pressure is the driving force that propels blood along the distribution network. The blood vessels include arteries, arterioles, capillaries, venules and veins. Blood is carried in these vessels. The arteries, which are strong, flexible, and resilient, carry blood away from the heart and bear the highest blood pressures. Because arteries are elastic, they recoil passively when the heart is relaxing between beats, thus helping to maintain blood pressure. Arteries branch into smaller and smaller vessels, eventually becoming very small vessels called arterioles. Arteries and arterioles have muscular walls that can adjust their diameters to increase or decrease blood flow to a particular part of the body.
The principal manifestations of CAD are coronary atherosclerosis (hardening of the coronary arteries) or stenosis (narrowing of the arteries), both of which ultimately force a reduction in coronary circulation and result in low blood flow and less blood volume from heart to body. During cardiac arrhythmia, various portions of heart muscle receive less oxygen that can ultimately lead to irreversible scarring and necrosis of the muscle tissue (myocardial infarction), reducing the efficiency with which the heart can pump blood to the rest of the body and possibly leading to fatal cardiac arrhythmias.
Hemodynamic signals (e.g., pressure, pulse, temperature and thermal signals) related to blood flow from heart to human body surface vessels may be analyzed to characterize cardiac pathology and disorders, and even predict life-threatening events. However, traditional methods focus on stroke volume and cardiac output calculation, which do not fully capture waveform information from the patient blood pressure signals. Other known clinical methods for cardiac arrhythmia detection require extensive clinical experience and knowledge of these approaches, such as interpretation of the parameters, calculation accuracy, etc., which may pose limitations for some medical users.
Known cardiac arrhythmia characterization approaches are mostly based on electrocardiography (ECG) and other electrophysiological activity signals. However, cardiac chamber malfunctions and clinical events affect the tissue and hemodynamic signals much earlier than electrophysiological signals.
Current hemodynamic parameter-based cardiac arrhythmia detection methods (e.g., invasive blood pressure or IBP) are typically invasive. Known thermodilution methods in heart calculation typically involve acquiring an invasive injection waveform. Current non-invasive methods include image-based cardiac pathology detection methods, such as fluroscopic image scanning and ultrasound image scanning, which require measurement of a two-dimensional (2D) or three-dimensional (3D) heart image to calculate its size and volume. Such methods are typically inaccurate, especially for end-of-diastole (EoD) and end-of-systole (EoS) timing-based size measurement.
Additionally, most known clinical methods for cardiac arrhythmia diagnosis focus on linear relations between signal waveform morphology and cardiac diseases. However, relations between cardiac arrhythmia and patient signals are actually nonlinear. Therefore, current clinical methods can only achieve non-accurate functionality diagnosis.
In addition, in most clinical environments (e.g., operating room, catheter lab, etc.), there is a lot of unwanted but unavoidable electrical noise, such as power emission noise, electrical stimulating noise, electrical cutter noise, etc. Such noise can easily affect electrophysiological signals (e.g., ECG signals).
Currently, traditional methods for cardiac arrhythmia analysis provide qualitative evaluation of cardiac events. There are no efficient methods for quantitative characterization of cardiac pathology severity. Furthermore, known cardiac arrhythmia event detection algorithms may cause false alarms arising from analysis of a single parameter, such as magnitude of ST segment from surface ECG data. Known medical applications also require better methods to more accurately and timely characterize and predict cardiac arrhythmia events.